
One-versus-rest classification is a widely used variant to resolve multi-class problems and as suggested for the online evolving case in [1]}, for standard multi-class classification problems (no multi-labels are investigated, just a single class vector): therein, the consequent functions are estimated separately and independently for each class, which thus can also be directly applied to the multi-label based indicator vectors stored in \(Y\)  (column-wise estimation).

Classifier chaining is a widely used variant to resolve multi-label problems [2]}: it performs a direct consequent learning for the first class solely based on the input feature space, a consequent learning for the second class based on the feature space joint with the indicator vector of the first class, a consequent learning for the third class based on the feature space joint with the indicator vectors of the first and second class and so on; thus, it internally considers some relations among labels when learning for subsequent classes (due to joint feature spaces), however it does not integrate an explicit objective term (and associated optimization) for label correlation/relation learning.

